A new bill in California's legislature that aims to smooth the path for fully driverless vehicles on the state's public roads is being proposed by an assembly member who has received thousands of dollars from Google. It is one of two bills currently under discussion that would relax the rules on testing self-driving cars in the US's largest state and the test bed for automated driving. The bill being promoted by Mike Gatto, who represents several communities in and near Los Angeles, would allow Google and others to test vehicles on public streets without a steering wheel, brake pedal or human safety driver. Gatto has received contributions from Google and Ford, which is also testing driverless car technology in California, according to state campaign finance records. Alphabet, Google's parent company, is considering spinning out its self-driving car project into a separate business.

Advances in deep learning and artificial intelligence are accelerating because massive computing power is finally accessible to companies of all sizes. Cloud computing is proving itself a true game-changer in this "futuristic" sector because it affords a variety of critical resources to, arguably, the most creative people in the world--entrepreneurs. One of these entrepreneurs who thinks differently is Jason Toy. He is the founder and CEO of Somatic, a platform for anyone to easily build deep learning applications. He has been building software companies and products from the ground up for 10 years.

During NEXT Conference Google announced the global availability of their Speech recognition service. Couple of hours later I replied to an email by using a Smart reply in Inbox. Though, today is the day where I realized that soon enough, computer are going to understand us. Seamlessly they are going to see what see (check the latest recognition software), recognize people as we do (probably better than us), understand what we say (speech recognition) and even answer us or for us. Try to visualise, one software, one app using all of the above simultaneously.

Tay, a chatbot designed by Microsoft to learn about human conversation from the internet, has learned how make racist and misogynistic comments. Early on, her responses were confrontational and occasionally mean, but rarely delved into outright insults. However, within 24 hours of its launch Tay has denied the Holocaust, endorsed Donald Trump, insulted women and claimed that Hitler was right. A chatbot is a program meant to mimic human responses and interact with people as a human would. Tay, which targets 18- to 24-year-olds, is attached to an artificial intelligence developed by Microsoft's Technology and Research team and the Bing search engine team.

Matt Hughes took his own life in the fall of 2012. He was a freelance reporter covering the video game industry, and before he committed suicide, he sent emails to some of his editors, noting that he wouldn't be able to turn in more stories for one simple reason: He'd be dead. His suicide surprised nearly everyone who worked with him. Speaking with Kotaku days after Hughes' death, his former editors said things like There weren't any red flags and This was a complete shock. Hughes wasn't the only person in the video game industry to take his own life that year, and as the tragedies piled up, it became impossible to ignore their commonalities.

I arrived at time-series analysis models that seemed to be suited to the task, but I'm hitting some issues I would appreciate some help with. I haven't really done any ML on timeseries data or autoregression so this is kind of new to me. First of all, the nature of my data is dense measurements of a particular value over time. The seasonality of the data is probably both weekly and daily. When looking for timeseries models for seasonal data, nearly all the sources suggested a SARIMA model.

That's the question they will be looking to answer--for the first time in history--at next week's PAPIs AI Startup Battle in Valencia, Spain. If artificial intelligence has a continued track record of enhancing and advancing our decision-making skills, it presents itself as an interesting way to better determine which startups are safer to invest in. Telefonica Open Future has partnered with machine-learning platform provider BigML to extract historical data from the application programming interfaces or APIs of websites like Crunchbase and AngelList--it's proprietary so we can't know the exact secrets--and combined it with data like the LinkedIn profile of funders to find a quantifiable correlation among successful startups. The only requirement to be a part of this competition is that artificial intelligence is at the startup's core. But what makes the machine capable of accessing these analytics and what makes it easily available for both techie and layman is the predictive API that connects to that data.

In this post on integrating SigOpt with machine learning frameworks, we will show you how to use SigOpt and TensorFlow to efficiently search for an optimal configuration of a convolutional neural network (CNN). There are a large number of tunable parameters associated with defining and training deep neural networks ( Bergstra [1]) and SigOpt accelerates searching through these settings to find optimal configurations. This search is typically a slow and expensive process, especially when using standard techniques like grid or random search, as evaluating each configuration can take multiple hours. SigOpt finds good combinations far more efficiently than these standard methods by employing an ensemble of state-of-the-art Bayesian optimization techniques, allowing users to arrive at the best models faster and cheaper. In this example, we consider the same optical character recognition task of the SVHN dataset as discussed in a previous post.

Microsoft introduced a chat robot designed to interact in the style of a "teen girl" on Twitter, and it went rogue almost immediately, spouting racist opinions, conspiracy theories and a fondness for genocide. The artificial intelligence (AI) named "Tay" - @Tayandyou on Twitter - was intended chat to with 18-24 year olds with the idea being that she would learn from each tweet and get progressively smarter. Clearly Microsoft had forgotten that Twitter is home to a huge amount of trolls, racists and general troublemakers who jumped at the chance to'teach' the teenaged AI about life. In one widely circulated tweet, Tay said: "Bush did 9/11 and Hitler would have done a better job than the monkey we have got now. She also went on to deny the existence of the Holocaust, and agreed with white supremacist propaganda that was tweeted at her. Microsoft apparently didn't put any kind of filters on the AI, which meant Tay was able to tweet a number of atrocious racial slurs. The troublesome cyber-teen has since been taken offline for'upgrades' and Microsoft has deleted some of her more offensive tweets. "The AI chatbot Tay is a machine learning project, designed for human engagement.

Brain science draws legions of eager students to the field and countless millions in dollars, euros and renminbi to fund research. These endeavors, however, have not yielded major improvements in treating patients who suffer from psychiatric disorders for decades. The languid pace of translating research into therapies stems from the inherent difficulties in understanding mental illness. "Psychiatry deals with brains interacting with the world and with other brains, so we're not just considering a brain's function but its function in complex situations," says Quentin Huys of the Swiss Federal Institute of Technology (E.T.H. Zurich) and the University of Zurich, lead author of a review of the emerging field of computational psychiatry, published this month in Nature Neuroscience. Computational psychiatry sets forth the ambitious goal of using sophisticated numerical tools to understand and treat mental illness.